Overview

Brought to you by YData

Dataset statistics

Number of variables73
Number of observations501.729
Missing cells4.757.170
Missing cells (%)13.0%
Total size in memory279.4 MiB
Average record size in memory584.0 B

Variable types

Numeric41
Text23
Unsupported9

Alerts

COD_EVE has constant value "210"Constant
CON_FIN has constant value "1"Constant
va_sispro has constant value "1"Constant
Nombre_evento has constant value "DENGUE"Constant
COD_ASE has 9817 (2.0%) missing valuesMissing
GRU_POB has 501729 (100.0%) missing valuesMissing
nom_grupo has 306292 (61.0%) missing valuesMissing
estrato has 13144 (2.6%) missing valuesMissing
sem_ges has 308003 (61.4%) missing valuesMissing
FEC_HOS has 303783 (60.5%) missing valuesMissing
FEC_DEF has 501729 (100.0%) missing valuesMissing
CER_DEF has 501729 (100.0%) missing valuesMissing
CBMTE has 501729 (100.0%) missing valuesMissing
FM_FUERZA has 498738 (99.4%) missing valuesMissing
FM_UNIDAD has 498741 (99.4%) missing valuesMissing
FM_GRADO has 498741 (99.4%) missing valuesMissing
consecutive_origen has 309627 (61.7%) missing valuesMissing
COD_PAIS_O is highly skewed (γ1 = 26.99818486)Skewed
GP_DISCAPA is highly skewed (γ1 = -26.23151426)Skewed
GP_CARCELA is highly skewed (γ1 = -46.27260847)Skewed
GP_INDIGEN is highly skewed (γ1 = -64.37037889)Skewed
GP_POBICFB is highly skewed (γ1 = -49.2018951)Skewed
GP_MAD_COM is highly skewed (γ1 = -121.4653184)Skewed
GP_DESMOVI is highly skewed (γ1 = -82.32352211)Skewed
GP_PSIQUIA is highly skewed (γ1 = -63.079317)Skewed
GP_VIC_VIO is highly skewed (γ1 = -24.21943434)Skewed
COD_PAIS_R is highly skewed (γ1 = 29.73851511)Skewed
CONSECUTIVE has unique valuesUnique
OCUPACION is an unsupported type, check if it needs cleaning or further analysisUnsupported
GRU_POB is an unsupported type, check if it needs cleaning or further analysisUnsupported
estrato is an unsupported type, check if it needs cleaning or further analysisUnsupported
sem_ges is an unsupported type, check if it needs cleaning or further analysisUnsupported
FEC_DEF is an unsupported type, check if it needs cleaning or further analysisUnsupported
CER_DEF is an unsupported type, check if it needs cleaning or further analysisUnsupported
CBMTE is an unsupported type, check if it needs cleaning or further analysisUnsupported
FM_UNIDAD is an unsupported type, check if it needs cleaning or further analysisUnsupported
FM_GRADO is an unsupported type, check if it needs cleaning or further analysisUnsupported
AJUSTE has 264739 (52.8%) zerosZeros
confirmados has 113782 (22.7%) zerosZeros

Reproduction

Analysis started2025-10-29 03:37:14.949771
Analysis finished2025-10-29 03:37:24.251269
Duration9.3 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

CONSECUTIVE
Real number (ℝ)

Unique 

Distinct501729
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10740940.04
Minimum8908033
Maximum11747641
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:24.319180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8908033
5-th percentile9151413.4
Q110302063
median10960600
Q311312242
95-th percentile11567980.6
Maximum11747641
Range2839608
Interquartile range (IQR)1010179

Descriptive statistics

Standard deviation729242.2158
Coefficient of variation (CV)0.06789370515
Kurtosis-0.2742943123
Mean10740940.04
Median Absolute Deviation (MAD)446698
Skewness-0.8810943457
Sum5.389041103 × 1012
Variance5.317942093 × 1011
MonotonicityNot monotonic
2025-10-28T22:37:24.440898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116527711
 
< 0.1%
117475041
 
< 0.1%
115500541
 
< 0.1%
111903771
 
< 0.1%
112841061
 
< 0.1%
116527211
 
< 0.1%
117476391
 
< 0.1%
115421041
 
< 0.1%
110502631
 
< 0.1%
117476321
 
< 0.1%
Other values (501719)501719
> 99.9%
ValueCountFrequency (%)
89080331
< 0.1%
89080341
< 0.1%
89080351
< 0.1%
89080361
< 0.1%
89080371
< 0.1%
ValueCountFrequency (%)
117476411
< 0.1%
117476401
< 0.1%
117476391
< 0.1%
117476361
< 0.1%
117476341
< 0.1%

COD_EVE
Real number (ℝ)

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210
Minimum210
Maximum210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:24.541704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum210
5-th percentile210
Q1210
median210
Q3210
95-th percentile210
Maximum210
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean210
Median Absolute Deviation (MAD)0
Skewness0
Sum105363090
Variance0
MonotonicityIncreasing
2025-10-28T22:37:24.610582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
210501729
100.0%
ValueCountFrequency (%)
210501729
100.0%
ValueCountFrequency (%)
210501729
100.0%

FEC_NOT
Text

Distinct1170
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:24.851532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5.017.290
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row2022-09-30
2nd row2022-02-07
3rd row2022-04-08
4th row2022-04-04
5th row2022-05-16
ValueCountFrequency (%)
2024-06-171787
 
0.4%
2024-06-041594
 
0.3%
2024-05-201586
 
0.3%
2024-06-111584
 
0.3%
2024-05-271581
 
0.3%
2024-05-141550
 
0.3%
2024-05-311536
 
0.3%
2024-06-071525
 
0.3%
2024-06-241513
 
0.3%
2024-05-061508
 
0.3%
Other values (1160)485965
96.9%
2025-10-28T22:37:25.165873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21363453
27.2%
01116268
22.2%
-1003458
20.0%
1408917
 
8.2%
4406544
 
8.1%
3238106
 
4.7%
5104140
 
2.1%
6101535
 
2.0%
798286
 
2.0%
890795
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)5017290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21363453
27.2%
01116268
22.2%
-1003458
20.0%
1408917
 
8.2%
4406544
 
8.1%
3238106
 
4.7%
5104140
 
2.1%
6101535
 
2.0%
798286
 
2.0%
890795
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5017290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21363453
27.2%
01116268
22.2%
-1003458
20.0%
1408917
 
8.2%
4406544
 
8.1%
3238106
 
4.7%
5104140
 
2.1%
6101535
 
2.0%
798286
 
2.0%
890795
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5017290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21363453
27.2%
01116268
22.2%
-1003458
20.0%
1408917
 
8.2%
4406544
 
8.1%
3238106
 
4.7%
5104140
 
2.1%
6101535
 
2.0%
798286
 
2.0%
890795
 
1.8%

SEMANA
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.3339791
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:25.268654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q115
median25
Q338
95-th percentile50
Maximum52
Range51
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.38047025
Coefficient of variation (CV)0.5460804156
Kurtosis-1.064144711
Mean26.3339791
Median Absolute Deviation (MAD)12
Skewness0.09642017339
Sum13212521
Variance206.7979246
MonotonicityNot monotonic
2025-10-28T22:37:25.434395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2113006
 
2.6%
2212780
 
2.5%
2312365
 
2.5%
2412266
 
2.4%
2012193
 
2.4%
1911725
 
2.3%
2511721
 
2.3%
1811716
 
2.3%
2911473
 
2.3%
2711424
 
2.3%
Other values (42)381060
75.9%
ValueCountFrequency (%)
17740
1.5%
27334
1.5%
37656
1.5%
47566
1.5%
58196
1.6%
ValueCountFrequency (%)
529499
1.9%
519641
1.9%
5010304
2.1%
4910037
2.0%
489070
1.8%

ANO
Real number (ℝ)

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2023.486191
Minimum2022
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:25.543437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2022
5-th percentile2022
Q12023
median2024
Q32024
95-th percentile2024
Maximum2024
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7153102948
Coefficient of variation (CV)0.0003535039172
Kurtosis-0.3421067669
Mean2023.486191
Median Absolute Deviation (MAD)0
Skewness-1.024700539
Sum1015241703
Variance0.5116688178
MonotonicityIncreasing
2025-10-28T22:37:25.639013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2024309627
61.7%
2023126411
25.2%
202265691
 
13.1%
ValueCountFrequency (%)
202265691
 
13.1%
2023126411
25.2%
2024309627
61.7%
ValueCountFrequency (%)
2024309627
61.7%
2023126411
25.2%
202265691
 
13.1%

COD_PRE
Real number (ℝ)

Distinct2888
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5166173141
Minimum500100000
Maximum9977384009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:25.757324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum500100000
5-th percentile800100037
Q12318200520
median6600101912
Q37600102541
95-th percentile8130000206
Maximum9977384009
Range9477284009
Interquartile range (IQR)5281902021

Descriptive statistics

Standard deviation2675820952
Coefficient of variation (CV)0.5179503046
Kurtosis-1.293822727
Mean5166173141
Median Absolute Deviation (MAD)1316599993
Skewness-0.4861555427
Sum2.592018884 × 1015
Variance7.160017767 × 1018
MonotonicityNot monotonic
2025-10-28T22:37:25.889112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76001025416628
 
1.3%
76001025346235
 
1.2%
8001044546104
 
1.2%
76001000374747
 
0.9%
73268007944586
 
0.9%
68001011574486
 
0.9%
76001115924303
 
0.9%
41001004514197
 
0.8%
68001702764184
 
0.8%
76892040734143
 
0.8%
Other values (2878)452116
90.1%
ValueCountFrequency (%)
50010000030
 
< 0.1%
50010001211
 
< 0.1%
500101150320
0.1%
5001015332
 
< 0.1%
50010153953
 
< 0.1%
ValueCountFrequency (%)
99773840092
 
< 0.1%
9977300145137
< 0.1%
99773001301
 
< 0.1%
9977300006106
< 0.1%
996240000650
 
< 0.1%

COD_SUB
Real number (ℝ)

Distinct82
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.860502383
Minimum0
Maximum99
Zeros1308
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:26.012886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile30
Maximum99
Range99
Interquartile range (IQR)1

Descriptive statistics

Standard deviation11.4085577
Coefficient of variation (CV)2.347197224
Kurtosis21.10273377
Mean4.860502383
Median Absolute Deviation (MAD)0
Skewness4.331588015
Sum2438655
Variance130.1551888
MonotonicityNot monotonic
2025-10-28T22:37:26.135257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1355127
70.8%
223541
 
4.7%
322819
 
4.5%
69591
 
1.9%
79338
 
1.9%
49254
 
1.8%
107366
 
1.5%
57026
 
1.4%
83714
 
0.7%
303570
 
0.7%
Other values (72)50383
 
10.0%
ValueCountFrequency (%)
01308
 
0.3%
1355127
70.8%
223541
 
4.7%
322819
 
4.5%
49254
 
1.8%
ValueCountFrequency (%)
9957
 
< 0.1%
931
 
< 0.1%
83454
0.1%
82458
0.1%
81785
0.2%

EDAD
Real number (ℝ)

Distinct108
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.45874167
Minimum1
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:26.271885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q110
median17
Q332
95-th percentile64
Maximum122
Range121
Interquartile range (IQR)22

Descriptive statistics

Standard deviation18.81400762
Coefficient of variation (CV)0.8020041265
Kurtosis0.9997094375
Mean23.45874167
Median Absolute Deviation (MAD)9
Skewness1.258631557
Sum11769931
Variance353.9668828
MonotonicityNot monotonic
2025-10-28T22:37:26.398581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1119734
 
3.9%
1019604
 
3.9%
919261
 
3.8%
818635
 
3.7%
1218508
 
3.7%
1318104
 
3.6%
1417915
 
3.6%
717354
 
3.5%
1516603
 
3.3%
615949
 
3.2%
Other values (98)320062
63.8%
ValueCountFrequency (%)
17155
1.4%
28112
1.6%
39569
1.9%
411210
2.2%
513490
2.7%
ValueCountFrequency (%)
1221
 
< 0.1%
1101
 
< 0.1%
1081
 
< 0.1%
1064
< 0.1%
1041
 
< 0.1%

UNI_MED
Real number (ℝ)

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.015211399
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:26.504827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum4
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1258929662
Coefficient of variation (CV)0.1240066515
Kurtosis79.80850833
Mean1.015211399
Median Absolute Deviation (MAD)0
Skewness8.614007967
Sum509361
Variance0.01584903893
MonotonicityNot monotonic
2025-10-28T22:37:26.586856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
1494307
98.5%
27220
 
1.4%
3194
 
< 0.1%
48
 
< 0.1%
ValueCountFrequency (%)
1494307
98.5%
27220
 
1.4%
3194
 
< 0.1%
48
 
< 0.1%
ValueCountFrequency (%)
48
 
< 0.1%
3194
 
< 0.1%
27220
 
1.4%
1494307
98.5%

nacionalidad
Real number (ℝ)

Distinct89
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.1485463
Minimum4
Maximum862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:26.697951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile170
Q1170
median170
Q3170
95-th percentile170
Maximum862
Range858
Interquartile range (IQR)0

Descriptive statistics

Standard deviation94.14905465
Coefficient of variation (CV)0.5140584326
Kurtosis47.61485384
Mean183.1485463
Median Absolute Deviation (MAD)0
Skewness7.035253391
Sum91890937
Variance8864.044492
MonotonicityNot monotonic
2025-10-28T22:37:26.831372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170491548
98.0%
8629231
 
1.8%
218140
 
< 0.1%
840100
 
< 0.1%
72465
 
< 0.1%
60464
 
< 0.1%
27656
 
< 0.1%
25052
 
< 0.1%
7639
 
< 0.1%
3239
 
< 0.1%
Other values (79)395
 
0.1%
ValueCountFrequency (%)
42
< 0.1%
82
< 0.1%
121
< 0.1%
161
< 0.1%
281
< 0.1%
ValueCountFrequency (%)
8629231
1.8%
8601
 
< 0.1%
8582
 
< 0.1%
8501
 
< 0.1%
840100
 
< 0.1%
Distinct134
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:26.985135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length100
Median length8
Mean length43.23714595
Min length4

Characters and Unicode

Total characters21.693.330
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA
ValueCountFrequency (%)
colombia491548
97.9%
venezuela9231
 
1.8%
ecuador140
 
< 0.1%
de123
 
< 0.1%
estados101
 
< 0.1%
unidos101
 
< 0.1%
américa100
 
< 0.1%
españa65
 
< 0.1%
perú64
 
< 0.1%
alemania56
 
< 0.1%
Other values (105)729
 
0.1%
2025-10-28T22:37:27.234012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17668944
81.4%
O983640
 
4.5%
A502392
 
2.3%
L501089
 
2.3%
I492325
 
2.3%
C492104
 
2.3%
M491778
 
2.3%
B491707
 
2.3%
E28529
 
0.1%
N9760
 
< 0.1%
Other values (23)31062
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)21693330
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17668944
81.4%
O983640
 
4.5%
A502392
 
2.3%
L501089
 
2.3%
I492325
 
2.3%
C492104
 
2.3%
M491778
 
2.3%
B491707
 
2.3%
E28529
 
0.1%
N9760
 
< 0.1%
Other values (23)31062
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21693330
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17668944
81.4%
O983640
 
4.5%
A502392
 
2.3%
L501089
 
2.3%
I492325
 
2.3%
C492104
 
2.3%
M491778
 
2.3%
B491707
 
2.3%
E28529
 
0.1%
N9760
 
< 0.1%
Other values (23)31062
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21693330
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17668944
81.4%
O983640
 
4.5%
A502392
 
2.3%
L501089
 
2.3%
I492325
 
2.3%
C492104
 
2.3%
M491778
 
2.3%
B491707
 
2.3%
E28529
 
0.1%
N9760
 
< 0.1%
Other values (23)31062
 
0.1%

SEXO
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:27.297774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters501.729
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowM
5th rowM
ValueCountFrequency (%)
m253726
50.6%
f248003
49.4%
2025-10-28T22:37:27.435722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M253726
50.6%
F248003
49.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)501729
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M253726
50.6%
F248003
49.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)501729
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M253726
50.6%
F248003
49.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)501729
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M253726
50.6%
F248003
49.4%

COD_PAIS_O
Real number (ℝ)

Skewed 

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.9026765
Minimum4
Maximum862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:27.530550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile170
Q1170
median170
Q3170
95-th percentile170
Maximum862
Range858
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.69146947
Coefficient of variation (CV)0.1444767862
Kurtosis740.9136232
Mean170.9026765
Median Absolute Deviation (MAD)0
Skewness26.99818486
Sum85746829
Variance609.6686645
MonotonicityNot monotonic
2025-10-28T22:37:27.710502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
170500760
99.8%
862585
 
0.1%
76118
 
< 0.1%
60457
 
< 0.1%
48436
 
< 0.1%
21430
 
< 0.1%
19219
 
< 0.1%
21819
 
< 0.1%
32014
 
< 0.1%
3213
 
< 0.1%
Other values (35)78
 
< 0.1%
ValueCountFrequency (%)
41
 
< 0.1%
81
 
< 0.1%
3213
< 0.1%
361
 
< 0.1%
441
 
< 0.1%
ValueCountFrequency (%)
862585
0.1%
8406
 
< 0.1%
8263
 
< 0.1%
8181
 
< 0.1%
7242
 
< 0.1%

COD_DPTO_O
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.72974853
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:27.826181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q123
median66
Q376
95-th percentile81
Maximum99
Range98
Interquartile range (IQR)53

Descriptive statistics

Standard deviation26.60508514
Coefficient of variation (CV)0.5143091914
Kurtosis-1.261346292
Mean51.72974853
Median Absolute Deviation (MAD)12
Skewness-0.5035320267
Sum25954315
Variance707.8305552
MonotonicityNot monotonic
2025-10-28T22:37:27.933969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
76116309
23.2%
6848988
 
9.8%
7340474
 
8.1%
1328519
 
5.7%
5025907
 
5.2%
4125579
 
5.1%
524800
 
4.9%
823764
 
4.7%
2518618
 
3.7%
2315552
 
3.1%
Other values (23)133219
26.6%
ValueCountFrequency (%)
1969
 
0.2%
524800
4.9%
823764
4.7%
1328519
5.7%
152343
 
0.5%
ValueCountFrequency (%)
99720
 
0.1%
97525
 
0.1%
952004
0.4%
94508
 
0.1%
912115
0.4%

COD_MUN_O
Real number (ℝ)

Distinct544
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274.4561128
Minimum1
Maximum980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:28.046506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median182
Q3520
95-th percentile835
Maximum980
Range979
Interquartile range (IQR)519

Descriptive statistics

Standard deviation294.6550988
Coefficient of variation (CV)1.073596415
Kurtosis-0.9298702229
Mean274.4561128
Median Absolute Deviation (MAD)181
Skewness0.6709006098
Sum137702591
Variance86821.62723
MonotonicityNot monotonic
2025-10-28T22:37:28.367289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1190160
37.9%
52010111
 
2.0%
2768466
 
1.7%
3077414
 
1.5%
3646530
 
1.3%
8926025
 
1.2%
5475729
 
1.1%
1115289
 
1.1%
8344917
 
1.0%
7584865
 
1.0%
Other values (534)252223
50.3%
ValueCountFrequency (%)
1190160
37.9%
21
 
< 0.1%
3694
 
0.1%
43
 
< 0.1%
64066
 
0.8%
ValueCountFrequency (%)
980228
< 0.1%
96060
 
< 0.1%
8994
 
< 0.1%
89834
 
< 0.1%
8971
 
< 0.1%

AREA
Real number (ℝ)

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.279816395
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:28.454040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6445496552
Coefficient of variation (CV)0.5036266591
Kurtosis2.558440896
Mean1.279816395
Median Absolute Deviation (MAD)0
Skewness2.057477624
Sum642121
Variance0.415444258
MonotonicityNot monotonic
2025-10-28T22:37:28.541210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
1415003
82.7%
353666
 
10.7%
233060
 
6.6%
ValueCountFrequency (%)
1415003
82.7%
233060
 
6.6%
353666
 
10.7%
ValueCountFrequency (%)
353666
 
10.7%
233060
 
6.6%
1415003
82.7%

OCUPACION
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size3.8 MiB

TIP_SS
Text

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:28.603446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters501.729
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowC
5th rowS
ValueCountFrequency (%)
s245020
48.8%
c224039
44.7%
p20434
 
4.1%
n6042
 
1.2%
i3371
 
0.7%
e2823
 
0.6%
2025-10-28T22:37:28.738413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S245020
48.8%
C224039
44.7%
P20434
 
4.1%
N6042
 
1.2%
I3371
 
0.7%
E2823
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)501729
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S245020
48.8%
C224039
44.7%
P20434
 
4.1%
N6042
 
1.2%
I3371
 
0.7%
E2823
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)501729
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S245020
48.8%
C224039
44.7%
P20434
 
4.1%
N6042
 
1.2%
I3371
 
0.7%
E2823
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)501729
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S245020
48.8%
C224039
44.7%
P20434
 
4.1%
N6042
 
1.2%
I3371
 
0.7%
E2823
 
0.6%

COD_ASE
Text

Missing 

Distinct135
Distinct (%)< 0.1%
Missing9817
Missing (%)2.0%
Memory size3.8 MiB
2025-10-28T22:37:28.981838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.99978858
Min length4

Characters and Unicode

Total characters2.951.368
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowEPS025
2nd rowEPS025
3rd rowEPS025
4th rowEPS044
5th rowEPSS34
ValueCountFrequency (%)
eps00548534
 
9.9%
epss4145699
 
9.3%
eps03737188
 
7.6%
eps01034696
 
7.1%
eps00232611
 
6.6%
ess02429163
 
5.9%
ess20721387
 
4.3%
epss3718725
 
3.8%
ess06218314
 
3.7%
ess11817555
 
3.6%
Other values (125)188040
38.2%
2025-10-28T22:37:29.342642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S690269
23.4%
0526297
17.8%
E472447
16.0%
P355111
12.0%
1194335
 
6.6%
2141009
 
4.8%
4118734
 
4.0%
598157
 
3.3%
795745
 
3.2%
381849
 
2.8%
Other values (11)177415
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2951368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S690269
23.4%
0526297
17.8%
E472447
16.0%
P355111
12.0%
1194335
 
6.6%
2141009
 
4.8%
4118734
 
4.0%
598157
 
3.3%
795745
 
3.2%
381849
 
2.8%
Other values (11)177415
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2951368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S690269
23.4%
0526297
17.8%
E472447
16.0%
P355111
12.0%
1194335
 
6.6%
2141009
 
4.8%
4118734
 
4.0%
598157
 
3.3%
795745
 
3.2%
381849
 
2.8%
Other values (11)177415
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2951368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S690269
23.4%
0526297
17.8%
E472447
16.0%
P355111
12.0%
1194335
 
6.6%
2141009
 
4.8%
4118734
 
4.0%
598157
 
3.3%
795745
 
3.2%
381849
 
2.8%
Other values (11)177415
 
6.0%

PER_ETN
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.887273807
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:29.446401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q16
median6
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6372639672
Coefficient of variation (CV)0.1082443229
Kurtosis48.84628045
Mean5.887273807
Median Absolute Deviation (MAD)0
Skewness-6.906504116
Sum2953816
Variance0.4061053638
MonotonicityNot monotonic
2025-10-28T22:37:29.534362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6476628
95.0%
516743
 
3.3%
17295
 
1.5%
3524
 
0.1%
2345
 
0.1%
4194
 
< 0.1%
ValueCountFrequency (%)
17295
1.5%
2345
 
0.1%
3524
 
0.1%
4194
 
< 0.1%
516743
3.3%
ValueCountFrequency (%)
6476628
95.0%
516743
 
3.3%
4194
 
< 0.1%
3524
 
0.1%
2345
 
0.1%

GRU_POB
Unsupported

Missing  Rejected  Unsupported 

Missing501729
Missing (%)100.0%
Memory size3.8 MiB

nom_grupo
Text

Missing 

Distinct176
Distinct (%)0.1%
Missing306292
Missing (%)61.0%
Memory size3.8 MiB
2025-10-28T22:37:29.738517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length100
Median length100
Mean length98.39639884
Min length3

Characters and Unicode

Total characters19.230.297
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
wayuu2006
25.5%
embera847
 
10.8%
zenu624
 
7.9%
nasa549
 
7.0%
tikuna377
 
4.8%
sikuani338
 
4.3%
cubeo234
 
3.0%
katio220
 
2.8%
pijao197
 
2.5%
awa196
 
2.5%
Other values (89)2273
28.9%
2025-10-28T22:37:30.037578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19187580
99.8%
A8652
 
< 0.1%
U6648
 
< 0.1%
N3104
 
< 0.1%
E3074
 
< 0.1%
I2764
 
< 0.1%
W2451
 
< 0.1%
Y2264
 
< 0.1%
O1933
 
< 0.1%
R1633
 
< 0.1%
Other values (16)10194
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)19230297
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
19187580
99.8%
A8652
 
< 0.1%
U6648
 
< 0.1%
N3104
 
< 0.1%
E3074
 
< 0.1%
I2764
 
< 0.1%
W2451
 
< 0.1%
Y2264
 
< 0.1%
O1933
 
< 0.1%
R1633
 
< 0.1%
Other values (16)10194
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)19230297
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
19187580
99.8%
A8652
 
< 0.1%
U6648
 
< 0.1%
N3104
 
< 0.1%
E3074
 
< 0.1%
I2764
 
< 0.1%
W2451
 
< 0.1%
Y2264
 
< 0.1%
O1933
 
< 0.1%
R1633
 
< 0.1%
Other values (16)10194
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)19230297
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
19187580
99.8%
A8652
 
< 0.1%
U6648
 
< 0.1%
N3104
 
< 0.1%
E3074
 
< 0.1%
I2764
 
< 0.1%
W2451
 
< 0.1%
Y2264
 
< 0.1%
O1933
 
< 0.1%
R1633
 
< 0.1%
Other values (16)10194
 
0.1%

estrato
Unsupported

Missing  Rejected  Unsupported 

Missing13144
Missing (%)2.6%
Memory size3.8 MiB

GP_DISCAPA
Real number (ℝ)

Skewed 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.998553004
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:30.113580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum2
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.03801191088
Coefficient of variation (CV)0.01901971617
Kurtosis686.0950752
Mean1.998553004
Median Absolute Deviation (MAD)0
Skewness-26.23151426
Sum1002732
Variance0.001444905368
MonotonicityNot monotonic
2025-10-28T22:37:30.188346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2501003
99.9%
1726
 
0.1%
ValueCountFrequency (%)
1726
 
0.1%
2501003
99.9%
ValueCountFrequency (%)
2501003
99.9%
1726
 
0.1%

GP_DESPLAZ
Real number (ℝ)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.996199143
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:30.260308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum2
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06153387419
Coefficient of variation (CV)0.03082551879
Kurtosis258.1049836
Mean1.996199143
Median Absolute Deviation (MAD)0
Skewness-16.12773867
Sum1001551
Variance0.003786417673
MonotonicityNot monotonic
2025-10-28T22:37:30.344599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2499822
99.6%
11907
 
0.4%
ValueCountFrequency (%)
11907
 
0.4%
2499822
99.6%
ValueCountFrequency (%)
2499822
99.6%
11907
 
0.4%

GP_MIGRANT
Real number (ℝ)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.989530205
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:30.421849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum2
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1017850651
Coefficient of variation (CV)0.05116035176
Kurtosis90.52434445
Mean1.989530205
Median Absolute Deviation (MAD)0
Skewness-9.618938798
Sum998205
Variance0.01036019948
MonotonicityNot monotonic
2025-10-28T22:37:30.493314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2496476
99.0%
15253
 
1.0%
ValueCountFrequency (%)
15253
 
1.0%
2496476
99.0%
ValueCountFrequency (%)
2496476
99.0%
15253
 
1.0%

GP_CARCELA
Real number (ℝ)

Skewed 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.999533613
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:30.561493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum2
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.02159098527
Coefficient of variation (CV)0.01079801066
Kurtosis2139.162822
Mean1.999533613
Median Absolute Deviation (MAD)0
Skewness-46.27260847
Sum1003224
Variance0.000466170645
MonotonicityNot monotonic
2025-10-28T22:37:30.633234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2501495
> 99.9%
1234
 
< 0.1%
ValueCountFrequency (%)
1234
 
< 0.1%
2501495
> 99.9%
ValueCountFrequency (%)
2501495
> 99.9%
1234
 
< 0.1%

GP_GESTAN
Real number (ℝ)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.994809947
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:30.707753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum2
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.07185489851
Coefficient of variation (CV)0.03602092451
Kurtosis187.6833667
Mean1.994809947
Median Absolute Deviation (MAD)0
Skewness-13.77253131
Sum1000854
Variance0.00516312644
MonotonicityNot monotonic
2025-10-28T22:37:30.784072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2499125
99.5%
12604
 
0.5%
ValueCountFrequency (%)
12604
 
0.5%
2499125
99.5%
ValueCountFrequency (%)
2499125
99.5%
12604
 
0.5%

sem_ges
Unsupported

Missing  Rejected  Unsupported 

Missing308003
Missing (%)61.4%
Memory size3.8 MiB

GP_INDIGEN
Real number (ℝ)

Skewed 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.999758834
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:30.877758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum2
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.01552766458
Coefficient of variation (CV)0.007764768589
Kurtosis4141.562187
Mean1.999758834
Median Absolute Deviation (MAD)0
Skewness-64.37037889
Sum1003337
Variance0.0002411083673
MonotonicityNot monotonic
2025-10-28T22:37:30.952883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2501608
> 99.9%
1121
 
< 0.1%
ValueCountFrequency (%)
1121
 
< 0.1%
2501608
> 99.9%
ValueCountFrequency (%)
2501608
> 99.9%
1121
 
< 0.1%

GP_POBICFB
Real number (ℝ)

Skewed 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.999587427
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:31.025861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum2
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.02030773071
Coefficient of variation (CV)0.0101559604
Kurtosis2418.836124
Mean1.999587427
Median Absolute Deviation (MAD)0
Skewness-49.2018951
Sum1003251
Variance0.0004124039267
MonotonicityNot monotonic
2025-10-28T22:37:31.096019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2501522
> 99.9%
1207
 
< 0.1%
ValueCountFrequency (%)
1207
 
< 0.1%
2501522
> 99.9%
ValueCountFrequency (%)
2501522
> 99.9%
1207
 
< 0.1%

GP_MAD_COM
Real number (ℝ)

Skewed 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.999932234
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:31.174503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum2
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.008231719699
Coefficient of variation (CV)0.004115999311
Kurtosis14751.88238
Mean1.999932234
Median Absolute Deviation (MAD)0
Skewness-121.4653184
Sum1003424
Variance6.77612092 × 10-5
MonotonicityNot monotonic
2025-10-28T22:37:31.254605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2501695
> 99.9%
134
 
< 0.1%
ValueCountFrequency (%)
134
 
< 0.1%
2501695
> 99.9%
ValueCountFrequency (%)
2501695
> 99.9%
134
 
< 0.1%

GP_DESMOVI
Real number (ℝ)

Skewed 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.99985251
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:31.337240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum2
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.01214366173
Coefficient of variation (CV)0.006072278666
Kurtosis6775.189299
Mean1.99985251
Median Absolute Deviation (MAD)0
Skewness-82.32352211
Sum1003384
Variance0.0001474685203
MonotonicityNot monotonic
2025-10-28T22:37:31.419073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2501655
> 99.9%
174
 
< 0.1%
ValueCountFrequency (%)
174
 
< 0.1%
2501655
> 99.9%
ValueCountFrequency (%)
2501655
> 99.9%
174
 
< 0.1%

GP_PSIQUIA
Real number (ℝ)

Skewed 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.999748868
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:31.509593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum2
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.01584515763
Coefficient of variation (CV)0.007923573745
Kurtosis3977.016087
Mean1.999748868
Median Absolute Deviation (MAD)0
Skewness-63.079317
Sum1003332
Variance0.0002510690203
MonotonicityNot monotonic
2025-10-28T22:37:31.589800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2501603
> 99.9%
1126
 
< 0.1%
ValueCountFrequency (%)
1126
 
< 0.1%
2501603
> 99.9%
ValueCountFrequency (%)
2501603
> 99.9%
1126
 
< 0.1%

GP_VIC_VIO
Real number (ℝ)

Skewed 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.998303865
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:31.668670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum2
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04114925598
Coefficient of variation (CV)0.02059209147
Kurtosis584.5833298
Mean1.998303865
Median Absolute Deviation (MAD)0
Skewness-24.21943434
Sum1002607
Variance0.001693261268
MonotonicityNot monotonic
2025-10-28T22:37:31.758098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2500878
99.8%
1851
 
0.2%
ValueCountFrequency (%)
1851
 
0.2%
2500878
99.8%
ValueCountFrequency (%)
2500878
99.8%
1851
 
0.2%

GP_OTROS
Real number (ℝ)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.023546576
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:31.851433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum2
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1516317267
Coefficient of variation (CV)0.1481434555
Kurtosis37.49351979
Mean1.023546576
Median Absolute Deviation (MAD)0
Skewness6.284375095
Sum513543
Variance0.02299218053
MonotonicityNot monotonic
2025-10-28T22:37:31.953049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1489915
97.6%
211814
 
2.4%
ValueCountFrequency (%)
1489915
97.6%
211814
 
2.4%
ValueCountFrequency (%)
211814
 
2.4%
1489915
97.6%

fuente
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.051167064
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:32.032305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2528161083
Coefficient of variation (CV)0.2405099217
Kurtosis56.10821089
Mean1.051167064
Median Absolute Deviation (MAD)0
Skewness6.397160251
Sum527401
Variance0.06391598461
MonotonicityNot monotonic
2025-10-28T22:37:32.160886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1478745
95.4%
221278
 
4.2%
3899
 
0.2%
4627
 
0.1%
5179
 
< 0.1%
01
 
< 0.1%
ValueCountFrequency (%)
01
 
< 0.1%
1478745
95.4%
221278
 
4.2%
3899
 
0.2%
4627
 
0.1%
ValueCountFrequency (%)
5179
 
< 0.1%
4627
 
0.1%
3899
 
0.2%
221278
 
4.2%
1478745
95.4%

COD_PAIS_R
Real number (ℝ)

Skewed 

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.7428552
Minimum4
Maximum862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:32.323529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile170
Q1170
median170
Q3170
95-th percentile170
Maximum862
Range858
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.68916673
Coefficient of variation (CV)0.1328850142
Kurtosis893.8851775
Mean170.7428552
Median Absolute Deviation (MAD)0
Skewness29.73851511
Sum85666642
Variance514.798287
MonotonicityNot monotonic
2025-10-28T22:37:32.476830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
170500974
99.8%
862486
 
0.1%
7685
 
< 0.1%
60421
 
< 0.1%
84021
 
< 0.1%
25015
 
< 0.1%
27613
 
< 0.1%
72411
 
< 0.1%
1407
 
< 0.1%
2187
 
< 0.1%
Other values (39)89
 
< 0.1%
ValueCountFrequency (%)
42
 
< 0.1%
122
 
< 0.1%
281
 
< 0.1%
326
< 0.1%
366
< 0.1%
ValueCountFrequency (%)
862486
0.1%
8581
 
< 0.1%
84021
 
< 0.1%
8265
 
< 0.1%
7602
 
< 0.1%

COD_DPTO_R
Real number (ℝ)

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.31437688
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:32.721712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q123
median66
Q376
95-th percentile81
Maximum99
Range98
Interquartile range (IQR)53

Descriptive statistics

Standard deviation26.80970767
Coefficient of variation (CV)0.5224599673
Kurtosis-1.300908839
Mean51.31437688
Median Absolute Deviation (MAD)14
Skewness-0.4800267791
Sum25745911
Variance718.7604254
MonotonicityNot monotonic
2025-10-28T22:37:32.856719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
76116013
23.1%
6848745
 
9.7%
7338913
 
7.8%
1328298
 
5.6%
5025539
 
5.1%
4125246
 
5.0%
525066
 
5.0%
823733
 
4.7%
2517421
 
3.5%
2315532
 
3.1%
Other values (24)137223
27.4%
ValueCountFrequency (%)
1755
 
0.2%
525066
5.0%
823733
4.7%
115035
 
1.0%
1328298
5.6%
ValueCountFrequency (%)
99699
 
0.1%
97513
 
0.1%
951970
0.4%
94520
 
0.1%
912117
0.4%

COD_MUN_R
Real number (ℝ)

Distinct562
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.8882445
Minimum1
Maximum980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:33.034290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median168
Q3520
95-th percentile835
Maximum980
Range979
Interquartile range (IQR)519

Descriptive statistics

Standard deviation294.1876832
Coefficient of variation (CV)1.094089047
Kurtosis-0.8981197647
Mean268.8882445
Median Absolute Deviation (MAD)167
Skewness0.6993103779
Sum134909030
Variance86546.39296
MonotonicityNot monotonic
2025-10-28T22:37:33.157214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1196647
39.2%
52010283
 
2.0%
2768323
 
1.7%
3077029
 
1.4%
3646195
 
1.2%
8925844
 
1.2%
5475672
 
1.1%
1115336
 
1.1%
8344973
 
1.0%
7584864
 
1.0%
Other values (552)246563
49.1%
ValueCountFrequency (%)
1196647
39.2%
21
 
< 0.1%
3683
 
0.1%
46
 
< 0.1%
63953
 
0.8%
ValueCountFrequency (%)
980220
< 0.1%
96056
 
< 0.1%
89937
 
< 0.1%
89830
 
< 0.1%
8971
 
< 0.1%

COD_DPTO_N
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.43275155
Minimum5
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:33.311692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q123
median66
Q376
95-th percentile81
Maximum99
Range94
Interquartile range (IQR)53

Descriptive statistics

Standard deviation26.75852332
Coefficient of variation (CV)0.5202623332
Kurtosis-1.296054037
Mean51.43275155
Median Absolute Deviation (MAD)14
Skewness-0.4854545527
Sum25805303
Variance716.0185703
MonotonicityNot monotonic
2025-10-28T22:37:33.428040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
76117121
23.3%
6848870
 
9.7%
7337612
 
7.5%
1327322
 
5.4%
5025525
 
5.1%
4125438
 
5.1%
524580
 
4.9%
824039
 
4.8%
2517340
 
3.5%
2316302
 
3.2%
Other values (23)137580
27.4%
ValueCountFrequency (%)
524580
4.9%
824039
4.8%
116531
 
1.3%
1327322
5.4%
152656
 
0.5%
ValueCountFrequency (%)
99663
 
0.1%
97508
 
0.1%
952002
0.4%
94540
 
0.1%
912207
0.4%

COD_MUN_N
Real number (ℝ)

Distinct934
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51661.69476
Minimum5001
Maximum99773
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:33.597563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile8001
Q123182
median66001
Q376001
95-th percentile81300
Maximum99773
Range94772
Interquartile range (IQR)52819

Descriptive statistics

Standard deviation26758.20616
Coefficient of variation (CV)0.5179506069
Kurtosis-1.293821014
Mean51661.69476
Median Absolute Deviation (MAD)13166
Skewness-0.486156076
Sum2.592017045 × 1010
Variance716001596.6
MonotonicityNot monotonic
2025-10-28T22:37:33.726708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7600158203
 
11.6%
6800123124
 
4.6%
7300118397
 
3.7%
800117388
 
3.5%
1300114256
 
2.8%
5000112552
 
2.5%
4100111237
 
2.2%
7652010265
 
2.0%
540019523
 
1.9%
230018936
 
1.8%
Other values (924)317848
63.4%
ValueCountFrequency (%)
50017619
1.5%
50021
 
< 0.1%
50041
 
< 0.1%
5030167
 
< 0.1%
503148
 
< 0.1%
ValueCountFrequency (%)
99773246
< 0.1%
9962452
 
< 0.1%
9952445
 
< 0.1%
99001320
0.1%
976661
 
< 0.1%

FEC_CON
Text

Distinct1120
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Memory size3.8 MiB
2025-10-28T22:37:34.064105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5.017.280
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st row2022-09-30
2nd row2022-02-07
3rd row2022-04-07
4th row2022-04-03
5th row2022-05-16
ValueCountFrequency (%)
2024-06-111685
 
0.3%
2024-05-271544
 
0.3%
2024-05-281536
 
0.3%
2024-06-121521
 
0.3%
2024-06-041511
 
0.3%
2024-05-201507
 
0.3%
2024-06-131498
 
0.3%
2024-06-171496
 
0.3%
2024-06-061479
 
0.3%
2024-05-231472
 
0.3%
Other values (1110)486479
97.0%
2025-10-28T22:37:34.391924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21362752
27.2%
01117649
22.3%
-1003456
20.0%
1408958
 
8.2%
4405963
 
8.1%
3239948
 
4.8%
5103448
 
2.1%
6100973
 
2.0%
798331
 
2.0%
890613
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)5017280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21362752
27.2%
01117649
22.3%
-1003456
20.0%
1408958
 
8.2%
4405963
 
8.1%
3239948
 
4.8%
5103448
 
2.1%
6100973
 
2.0%
798331
 
2.0%
890613
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5017280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21362752
27.2%
01117649
22.3%
-1003456
20.0%
1408958
 
8.2%
4405963
 
8.1%
3239948
 
4.8%
5103448
 
2.1%
6100973
 
2.0%
798331
 
2.0%
890613
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5017280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21362752
27.2%
01117649
22.3%
-1003456
20.0%
1408958
 
8.2%
4405963
 
8.1%
3239948
 
4.8%
5103448
 
2.1%
6100973
 
2.0%
798331
 
2.0%
890613
 
1.8%

INI_SIN
Text

Distinct1092
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:34.655660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5.017.290
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-09-27
2nd row2022-02-04
3rd row2022-04-01
4th row2022-03-31
5th row2022-05-12
ValueCountFrequency (%)
2024-05-201563
 
0.3%
2024-06-011531
 
0.3%
2024-04-011488
 
0.3%
2024-06-101469
 
0.3%
2024-05-251404
 
0.3%
2024-06-091392
 
0.3%
2024-05-241390
 
0.3%
2024-05-191387
 
0.3%
2024-05-271376
 
0.3%
2024-06-081368
 
0.3%
Other values (1082)487361
97.1%
2025-10-28T22:37:35.002024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21357523
27.1%
01125599
22.4%
-1003458
20.0%
1412924
 
8.2%
4403405
 
8.0%
3240894
 
4.8%
5104587
 
2.1%
698991
 
2.0%
796611
 
1.9%
889229
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)5017290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21357523
27.1%
01125599
22.4%
-1003458
20.0%
1412924
 
8.2%
4403405
 
8.0%
3240894
 
4.8%
5104587
 
2.1%
698991
 
2.0%
796611
 
1.9%
889229
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5017290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21357523
27.1%
01125599
22.4%
-1003458
20.0%
1412924
 
8.2%
4403405
 
8.0%
3240894
 
4.8%
5104587
 
2.1%
698991
 
2.0%
796611
 
1.9%
889229
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5017290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21357523
27.1%
01125599
22.4%
-1003458
20.0%
1412924
 
8.2%
4403405
 
8.0%
3240894
 
4.8%
5104587
 
2.1%
698991
 
2.0%
796611
 
1.9%
889229
 
1.8%

TIP_CAS
Real number (ℝ)

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.423116862
Minimum2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:35.083051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2
Q33
95-th percentile3
Maximum5
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5239962992
Coefficient of variation (CV)0.2162488766
Kurtosis0.8202393084
Mean2.423116862
Median Absolute Deviation (MAD)0
Skewness0.8394150085
Sum1215748
Variance0.2745721215
MonotonicityNot monotonic
2025-10-28T22:37:35.171759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2294537
58.7%
3204643
40.8%
52549
 
0.5%
ValueCountFrequency (%)
2294537
58.7%
3204643
40.8%
52549
 
0.5%
ValueCountFrequency (%)
52549
 
0.5%
3204643
40.8%
2294537
58.7%

PAC_HOS
Real number (ℝ)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.605472277
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:35.245883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum2
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4887495012
Coefficient of variation (CV)0.3044272444
Kurtosis-1.81372671
Mean1.605472277
Median Absolute Deviation (MAD)0
Skewness-0.4316022709
Sum805512
Variance0.2388760749
MonotonicityNot monotonic
2025-10-28T22:37:35.316178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2303783
60.5%
1197946
39.5%
ValueCountFrequency (%)
1197946
39.5%
2303783
60.5%
ValueCountFrequency (%)
2303783
60.5%
1197946
39.5%

FEC_HOS
Text

Missing 

Distinct1120
Distinct (%)0.6%
Missing303783
Missing (%)60.5%
Memory size3.8 MiB
2025-10-28T22:37:35.585386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1.979.460
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st row2022-04-07
2nd row2022-04-03
3rd row2022-05-17
4th row2022-07-16
5th row2022-05-15
ValueCountFrequency (%)
2024-05-23485
 
0.2%
2024-04-24479
 
0.2%
2024-03-21477
 
0.2%
2024-06-11469
 
0.2%
2024-05-29462
 
0.2%
2024-06-13459
 
0.2%
2024-05-14452
 
0.2%
2024-05-28446
 
0.2%
2024-06-06446
 
0.2%
2024-05-27438
 
0.2%
Other values (1110)193333
97.7%
2025-10-28T22:37:36.100964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2546935
27.6%
0441098
22.3%
-395892
20.0%
1163692
 
8.3%
4143576
 
7.3%
3102330
 
5.2%
738822
 
2.0%
538585
 
1.9%
638242
 
1.9%
836420
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1979460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2546935
27.6%
0441098
22.3%
-395892
20.0%
1163692
 
8.3%
4143576
 
7.3%
3102330
 
5.2%
738822
 
2.0%
538585
 
1.9%
638242
 
1.9%
836420
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1979460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2546935
27.6%
0441098
22.3%
-395892
20.0%
1163692
 
8.3%
4143576
 
7.3%
3102330
 
5.2%
738822
 
2.0%
538585
 
1.9%
638242
 
1.9%
836420
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1979460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2546935
27.6%
0441098
22.3%
-395892
20.0%
1163692
 
8.3%
4143576
 
7.3%
3102330
 
5.2%
738822
 
2.0%
538585
 
1.9%
638242
 
1.9%
836420
 
1.8%

CON_FIN
Real number (ℝ)

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:36.210572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum501729
Variance0
MonotonicityIncreasing
2025-10-28T22:37:36.312367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1501729
100.0%
ValueCountFrequency (%)
1501729
100.0%
ValueCountFrequency (%)
1501729
100.0%

FEC_DEF
Unsupported

Missing  Rejected  Unsupported 

Missing501729
Missing (%)100.0%
Memory size3.8 MiB

AJUSTE
Real number (ℝ)

Zeros 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.763071299
Minimum0
Maximum7
Zeros264739
Zeros (%)52.8%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:36.397948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.106281652
Coefficient of variation (CV)1.194666179
Kurtosis0.04490372457
Mean1.763071299
Median Absolute Deviation (MAD)0
Skewness0.9251989476
Sum884584
Variance4.436422399
MonotonicityNot monotonic
2025-10-28T22:37:36.498783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0264739
52.8%
3183138
36.5%
732955
 
6.6%
520897
 
4.2%
ValueCountFrequency (%)
0264739
52.8%
3183138
36.5%
520897
 
4.2%
732955
 
6.6%
ValueCountFrequency (%)
732955
 
6.6%
520897
 
4.2%
3183138
36.5%
0264739
52.8%
Distinct32369
Distinct (%)6.5%
Missing306
Missing (%)0.1%
Memory size3.8 MiB
2025-10-28T22:37:36.811393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5.014.230
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2.846 ?
Unique (%)0.6%

Sample

1st row2003-10-01
2nd row2003-11-03
3rd row2002-10-09
4th row2013-11-12
5th row2013-11-10
ValueCountFrequency (%)
2012-09-1382
 
< 0.1%
2012-06-2681
 
< 0.1%
2012-08-2180
 
< 0.1%
2012-09-1980
 
< 0.1%
2011-09-0979
 
< 0.1%
2013-04-3078
 
< 0.1%
2013-03-0177
 
< 0.1%
2012-06-1277
 
< 0.1%
2010-10-2377
 
< 0.1%
2012-07-1076
 
< 0.1%
Other values (32359)500636
99.8%
2025-10-28T22:37:37.270936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
01104437
22.0%
-1002846
20.0%
1838486
16.7%
2691770
13.8%
9392476
 
7.8%
8186385
 
3.7%
7169193
 
3.4%
3168488
 
3.4%
6161807
 
3.2%
5153931
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)5014230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01104437
22.0%
-1002846
20.0%
1838486
16.7%
2691770
13.8%
9392476
 
7.8%
8186385
 
3.7%
7169193
 
3.4%
3168488
 
3.4%
6161807
 
3.2%
5153931
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5014230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01104437
22.0%
-1002846
20.0%
1838486
16.7%
2691770
13.8%
9392476
 
7.8%
8186385
 
3.7%
7169193
 
3.4%
3168488
 
3.4%
6161807
 
3.2%
5153931
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5014230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01104437
22.0%
-1002846
20.0%
1838486
16.7%
2691770
13.8%
9392476
 
7.8%
8186385
 
3.7%
7169193
 
3.4%
3168488
 
3.4%
6161807
 
3.2%
5153931
 
3.1%

CER_DEF
Unsupported

Missing  Rejected  Unsupported 

Missing501729
Missing (%)100.0%
Memory size3.8 MiB

CBMTE
Unsupported

Missing  Rejected  Unsupported 

Missing501729
Missing (%)100.0%
Memory size3.8 MiB
Distinct1129
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:37.494941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5.017.290
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)< 0.1%

Sample

1st row2022-10-05
2nd row2022-02-16
3rd row2022-06-15
4th row2022-06-01
5th row2022-05-25
ValueCountFrequency (%)
2023-10-245897
 
1.2%
2022-09-073907
 
0.8%
2024-06-042357
 
0.5%
2024-06-172194
 
0.4%
2024-03-262171
 
0.4%
2024-05-272044
 
0.4%
2023-01-041962
 
0.4%
2024-04-021953
 
0.4%
2024-04-091952
 
0.4%
2024-06-241942
 
0.4%
Other values (1119)475350
94.7%
2025-10-28T22:37:37.874323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21357232
27.1%
01112071
22.2%
-1003458
20.0%
1419137
 
8.4%
4403629
 
8.0%
3233643
 
4.7%
5113008
 
2.3%
6100356
 
2.0%
794987
 
1.9%
890707
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)5017290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21357232
27.1%
01112071
22.2%
-1003458
20.0%
1419137
 
8.4%
4403629
 
8.0%
3233643
 
4.7%
5113008
 
2.3%
6100356
 
2.0%
794987
 
1.9%
890707
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5017290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21357232
27.1%
01112071
22.2%
-1003458
20.0%
1419137
 
8.4%
4403629
 
8.0%
3233643
 
4.7%
5113008
 
2.3%
6100356
 
2.0%
794987
 
1.9%
890707
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5017290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21357232
27.1%
01112071
22.2%
-1003458
20.0%
1419137
 
8.4%
4403629
 
8.0%
3233643
 
4.7%
5113008
 
2.3%
6100356
 
2.0%
794987
 
1.9%
890707
 
1.8%

FEC_AJU
Text

Distinct1182
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:38.109058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5.017.290
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row2022-10-03
2nd row2022-02-09
3rd row2022-06-08
4th row2022-05-26
5th row2022-05-18
ValueCountFrequency (%)
2023-10-246750
 
1.3%
2024-06-172309
 
0.5%
2024-05-272123
 
0.4%
2024-06-242023
 
0.4%
2024-05-202007
 
0.4%
2024-07-221886
 
0.4%
2024-03-111873
 
0.4%
2024-04-081827
 
0.4%
2024-07-291769
 
0.4%
2024-07-081740
 
0.3%
Other values (1172)477422
95.2%
2025-10-28T22:37:38.471479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21365796
27.2%
01108096
22.1%
-1003458
20.0%
1414765
 
8.3%
4403883
 
8.0%
3233967
 
4.7%
5112227
 
2.2%
698743
 
2.0%
795173
 
1.9%
891969
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)5017290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21365796
27.2%
01108096
22.1%
-1003458
20.0%
1414765
 
8.3%
4403883
 
8.0%
3233967
 
4.7%
5112227
 
2.2%
698743
 
2.0%
795173
 
1.9%
891969
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5017290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21365796
27.2%
01108096
22.1%
-1003458
20.0%
1414765
 
8.3%
4403883
 
8.0%
3233967
 
4.7%
5112227
 
2.2%
698743
 
2.0%
795173
 
1.9%
891969
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5017290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21365796
27.2%
01108096
22.1%
-1003458
20.0%
1414765
 
8.3%
4403883
 
8.0%
3233967
 
4.7%
5112227
 
2.2%
698743
 
2.0%
795173
 
1.9%
891969
 
1.8%

FM_FUERZA
Real number (ℝ)

Missing 

Distinct8
Distinct (%)0.3%
Missing498738
Missing (%)99.4%
Infinite0
Infinite (%)0.0%
Mean3.406552992
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:38.560573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median3
Q34
95-th percentile5
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7082012547
Coefficient of variation (CV)0.2078938024
Kurtosis5.030443211
Mean3.406552992
Median Absolute Deviation (MAD)0
Skewness1.918321738
Sum10189
Variance0.5015490172
MonotonicityNot monotonic
2025-10-28T22:37:38.652366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
32081
 
0.4%
4629
 
0.1%
5259
 
0.1%
711
 
< 0.1%
85
 
< 0.1%
12
 
< 0.1%
62
 
< 0.1%
22
 
< 0.1%
(Missing)498738
99.4%
ValueCountFrequency (%)
12
 
< 0.1%
22
 
< 0.1%
32081
0.4%
4629
 
0.1%
5259
 
0.1%
ValueCountFrequency (%)
85
 
< 0.1%
711
 
< 0.1%
62
 
< 0.1%
5259
0.1%
4629
0.1%

FM_UNIDAD
Unsupported

Missing  Rejected  Unsupported 

Missing498741
Missing (%)99.4%
Memory size3.8 MiB

FM_GRADO
Unsupported

Missing  Rejected  Unsupported 

Missing498741
Missing (%)99.4%
Memory size3.8 MiB

confirmados
Real number (ℝ)

Zeros 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7732202045
Minimum0
Maximum1
Zeros113782
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:38.732411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4187494111
Coefficient of variation (CV)0.5415655316
Kurtosis-0.2971344591
Mean0.7732202045
Median Absolute Deviation (MAD)0
Skewness-1.304939357
Sum387947
Variance0.1753510693
MonotonicityNot monotonic
2025-10-28T22:37:38.813468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1387947
77.3%
0113782
 
22.7%
ValueCountFrequency (%)
0113782
 
22.7%
1387947
77.3%
ValueCountFrequency (%)
1387947
77.3%
0113782
 
22.7%

consecutive_origen
Real number (ℝ)

Missing 

Distinct127189
Distinct (%)66.2%
Missing309627
Missing (%)61.7%
Infinite0
Infinite (%)0.0%
Mean53567.25754
Minimum1
Maximum128231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:38.937155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4845
Q124242.25
median48646
Q379494.75
95-th percentile118615.95
Maximum128231
Range128230
Interquartile range (IQR)55252.5

Descriptive statistics

Standard deviation35320.70376
Coefficient of variation (CV)0.6593711416
Kurtosis-0.8716431008
Mean53567.25754
Median Absolute Deviation (MAD)26534
Skewness0.4305079143
Sum1.029037731 × 1010
Variance1247552114
MonotonicityNot monotonic
2025-10-28T22:37:39.112420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
394082
 
< 0.1%
207722
 
< 0.1%
392642
 
< 0.1%
392652
 
< 0.1%
392662
 
< 0.1%
393072
 
< 0.1%
55962
 
< 0.1%
55972
 
< 0.1%
55982
 
< 0.1%
55992
 
< 0.1%
Other values (127179)192082
38.3%
(Missing)309627
61.7%
ValueCountFrequency (%)
12
< 0.1%
22
< 0.1%
32
< 0.1%
42
< 0.1%
52
< 0.1%
ValueCountFrequency (%)
1282311
< 0.1%
1282301
< 0.1%
1282291
< 0.1%
1282281
< 0.1%
1282271
< 0.1%

va_sispro
Real number (ℝ)

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:39.218661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum501729
Variance0
MonotonicityIncreasing
2025-10-28T22:37:39.291755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1501729
100.0%
ValueCountFrequency (%)
1501729
100.0%
ValueCountFrequency (%)
1501729
100.0%

Estado_final_de_caso
Real number (ℝ)

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.863866749
Minimum2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:39.359969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median3
Q33
95-th percentile3
Maximum5
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6241325166
Coefficient of variation (CV)0.2179335044
Kurtosis4.05313381
Mean2.863866749
Median Absolute Deviation (MAD)0
Skewness1.223316222
Sum1436885
Variance0.3895413983
MonotonicityNot monotonic
2025-10-28T22:37:39.444339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
3365207
72.8%
2113782
 
22.7%
522740
 
4.5%
ValueCountFrequency (%)
2113782
 
22.7%
3365207
72.8%
522740
 
4.5%
ValueCountFrequency (%)
522740
 
4.5%
3365207
72.8%
2113782
 
22.7%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:39.557589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length34
Median length26
Mean length22.28054986
Min length8

Characters and Unicode

Total characters11.178.798
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProbable
2nd rowProbable
3rd rowConfirmado por laboratorio
4th rowConfirmado por laboratorio
5th rowProbable
ValueCountFrequency (%)
confirmado387947
29.8%
por387947
29.8%
laboratorio365207
28.1%
probable113782
 
8.8%
nexo22740
 
1.7%
epidemiológico22740
 
1.7%
2025-10-28T22:37:39.748851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o2441464
21.8%
r1620090
14.5%
a1232143
11.0%
i821374
 
7.3%
798634
 
7.1%
b592771
 
5.3%
l501729
 
4.5%
d410687
 
3.7%
p410687
 
3.7%
m410687
 
3.7%
Other values (12)1938532
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)11178798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o2441464
21.8%
r1620090
14.5%
a1232143
11.0%
i821374
 
7.3%
798634
 
7.1%
b592771
 
5.3%
l501729
 
4.5%
d410687
 
3.7%
p410687
 
3.7%
m410687
 
3.7%
Other values (12)1938532
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11178798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o2441464
21.8%
r1620090
14.5%
a1232143
11.0%
i821374
 
7.3%
798634
 
7.1%
b592771
 
5.3%
l501729
 
4.5%
d410687
 
3.7%
p410687
 
3.7%
m410687
 
3.7%
Other values (12)1938532
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11178798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o2441464
21.8%
r1620090
14.5%
a1232143
11.0%
i821374
 
7.3%
798634
 
7.1%
b592771
 
5.3%
l501729
 
4.5%
d410687
 
3.7%
p410687
 
3.7%
m410687
 
3.7%
Other values (12)1938532
17.3%
Distinct3571
Distinct (%)0.7%
Missing3059
Missing (%)0.6%
Memory size3.8 MiB
2025-10-28T22:37:39.970874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length58
Median length41
Mean length32.6645096
Min length5

Characters and Unicode

Total characters16.288.811
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique616 ?
Unique (%)0.1%

Sample

1st rowESE HOSPITAL LOCAL DE TAURAMENA
2nd rowHOSPITAL DE AGUAZUL JUAN HERNANDO URREGO ESE
3rd rowHOSPITAL DE YOPAL ESE NUEVA SEDE
4th rowHOSPITAL DE YOPAL ESE NUEVA SEDE
5th rowHOSPITAL MUNICIPAL DE ACACIAS
ValueCountFrequency (%)
de205053
 
8.1%
hospital197952
 
7.8%
ese154428
 
6.1%
clinica114320
 
4.5%
san89650
 
3.5%
sa54338
 
2.1%
del51391
 
2.0%
salud46661
 
1.8%
ips44473
 
1.7%
sas42479
 
1.7%
Other values (3209)1544040
60.7%
2025-10-28T22:37:40.402376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2051145
12.6%
A2039267
12.5%
E1490860
9.2%
I1406025
 
8.6%
S1255761
 
7.7%
L1022891
 
6.3%
O967457
 
5.9%
N951234
 
5.8%
C805189
 
4.9%
D701099
 
4.3%
Other values (52)3597883
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)16288811
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2051145
12.6%
A2039267
12.5%
E1490860
9.2%
I1406025
 
8.6%
S1255761
 
7.7%
L1022891
 
6.3%
O967457
 
5.9%
N951234
 
5.8%
C805189
 
4.9%
D701099
 
4.3%
Other values (52)3597883
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16288811
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2051145
12.6%
A2039267
12.5%
E1490860
9.2%
I1406025
 
8.6%
S1255761
 
7.7%
L1022891
 
6.3%
O967457
 
5.9%
N951234
 
5.8%
C805189
 
4.9%
D701099
 
4.3%
Other values (52)3597883
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16288811
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2051145
12.6%
A2039267
12.5%
E1490860
9.2%
I1406025
 
8.6%
S1255761
 
7.7%
L1022891
 
6.3%
O967457
 
5.9%
N951234
 
5.8%
C805189
 
4.9%
D701099
 
4.3%
Other values (52)3597883
22.1%
Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:40.511764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length41
Median length8
Mean length8.001062326
Min length4

Characters and Unicode

Total characters4.014.365
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA
ValueCountFrequency (%)
colombia500760
99.8%
venezuela585
 
0.1%
brasil118
 
< 0.1%
perú57
 
< 0.1%
méxico36
 
< 0.1%
república30
 
< 0.1%
dominicana30
 
< 0.1%
cuba19
 
< 0.1%
ecuador19
 
< 0.1%
guatemala14
 
< 0.1%
Other values (54)148
 
< 0.1%
2025-10-28T22:37:40.721499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O1001661
25.0%
A501809
12.5%
L501534
12.5%
I501085
12.5%
B500947
12.5%
C500937
12.5%
M500866
12.5%
E1936
 
< 0.1%
N720
 
< 0.1%
U666
 
< 0.1%
Other values (20)2204
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4014365
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O1001661
25.0%
A501809
12.5%
L501534
12.5%
I501085
12.5%
B500947
12.5%
C500937
12.5%
M500866
12.5%
E1936
 
< 0.1%
N720
 
< 0.1%
U666
 
< 0.1%
Other values (20)2204
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4014365
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O1001661
25.0%
A501809
12.5%
L501534
12.5%
I501085
12.5%
B500947
12.5%
C500937
12.5%
M500866
12.5%
E1936
 
< 0.1%
N720
 
< 0.1%
U666
 
< 0.1%
Other values (20)2204
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4014365
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O1001661
25.0%
A501809
12.5%
L501534
12.5%
I501085
12.5%
B500947
12.5%
C500937
12.5%
M500866
12.5%
E1936
 
< 0.1%
N720
 
< 0.1%
U666
 
< 0.1%
Other values (20)2204
 
0.1%

Nombre_evento
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:40.794847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters3.010.374
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDENGUE
2nd rowDENGUE
3rd rowDENGUE
4th rowDENGUE
5th rowDENGUE
ValueCountFrequency (%)
dengue501729
100.0%
2025-10-28T22:37:40.971565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E1003458
33.3%
D501729
16.7%
N501729
16.7%
G501729
16.7%
U501729
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3010374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E1003458
33.3%
D501729
16.7%
N501729
16.7%
G501729
16.7%
U501729
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3010374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E1003458
33.3%
D501729
16.7%
N501729
16.7%
G501729
16.7%
U501729
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3010374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E1003458
33.3%
D501729
16.7%
N501729
16.7%
G501729
16.7%
U501729
16.7%
Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:41.094203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.928445436
Min length4

Characters and Unicode

Total characters3.476.202
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCASANARE
2nd rowCASANARE
3rd rowCASANARE
4th rowCASANARE
5th rowMETA
ValueCountFrequency (%)
valle116309
22.5%
santander64158
12.4%
tolima40474
 
7.8%
bolivar28519
 
5.5%
meta25907
 
5.0%
huila25579
 
4.9%
antioquia24800
 
4.8%
atlantico23764
 
4.6%
cundinamarca18618
 
3.6%
cordoba15552
 
3.0%
Other values (24)134059
25.9%
2025-10-28T22:37:41.339640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A724325
20.8%
L370457
10.7%
E272495
 
7.8%
N262877
 
7.6%
T230996
 
6.6%
I230306
 
6.6%
R218902
 
6.3%
O198655
 
5.7%
C158991
 
4.6%
V148077
 
4.3%
Other values (15)660121
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)3476202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A724325
20.8%
L370457
10.7%
E272495
 
7.8%
N262877
 
7.6%
T230996
 
6.6%
I230306
 
6.6%
R218902
 
6.3%
O198655
 
5.7%
C158991
 
4.6%
V148077
 
4.3%
Other values (15)660121
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3476202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A724325
20.8%
L370457
10.7%
E272495
 
7.8%
N262877
 
7.6%
T230996
 
6.6%
I230306
 
6.6%
R218902
 
6.3%
O198655
 
5.7%
C158991
 
4.6%
V148077
 
4.3%
Other values (15)660121
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3476202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A724325
20.8%
L370457
10.7%
E272495
 
7.8%
N262877
 
7.6%
T230996
 
6.6%
I230306
 
6.6%
R218902
 
6.3%
O198655
 
5.7%
C158991
 
4.6%
V148077
 
4.3%
Other values (15)660121
19.0%
Distinct970
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:41.610678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length29
Mean length8.405160953
Min length4

Characters and Unicode

Total characters4.217.113
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)< 0.1%

Sample

1st rowTAURAMENA
2nd rowAGUAZUL
3rd rowTAURAMENA
4th rowYOPAL
5th rowACACIAS
ValueCountFrequency (%)
cali51550
 
8.2%
san20577
 
3.3%
ibague17783
 
2.8%
de15988
 
2.6%
bucaramanga15485
 
2.5%
barranquilla14437
 
2.3%
cartagena11967
 
1.9%
la11828
 
1.9%
villavicencio11530
 
1.8%
el10762
 
1.7%
Other values (995)443214
70.9%
2025-10-28T22:37:41.978941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A807949
19.2%
I361440
 
8.6%
L315211
 
7.5%
E294909
 
7.0%
R291831
 
6.9%
N249970
 
5.9%
C249454
 
5.9%
O246181
 
5.8%
U203426
 
4.8%
T143949
 
3.4%
Other values (27)1052793
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)4217113
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A807949
19.2%
I361440
 
8.6%
L315211
 
7.5%
E294909
 
7.0%
R291831
 
6.9%
N249970
 
5.9%
C249454
 
5.9%
O246181
 
5.8%
U203426
 
4.8%
T143949
 
3.4%
Other values (27)1052793
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4217113
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A807949
19.2%
I361440
 
8.6%
L315211
 
7.5%
E294909
 
7.0%
R291831
 
6.9%
N249970
 
5.9%
C249454
 
5.9%
O246181
 
5.8%
U203426
 
4.8%
T143949
 
3.4%
Other values (27)1052793
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4217113
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A807949
19.2%
I361440
 
8.6%
L315211
 
7.5%
E294909
 
7.0%
R291831
 
6.9%
N249970
 
5.9%
C249454
 
5.9%
O246181
 
5.8%
U203426
 
4.8%
T143949
 
3.4%
Other values (27)1052793
25.0%
Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:42.105044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length41
Median length8
Mean length8.001811735
Min length4

Characters and Unicode

Total characters4.014.741
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA
ValueCountFrequency (%)
colombia500974
99.8%
venezuela486
 
0.1%
brasil85
 
< 0.1%
de27
 
< 0.1%
perú21
 
< 0.1%
estados21
 
< 0.1%
unidos21
 
< 0.1%
américa21
 
< 0.1%
francia15
 
< 0.1%
alemania13
 
< 0.1%
Other values (58)175
 
< 0.1%
2025-10-28T22:37:42.345884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O1002052
25.0%
A501901
12.5%
L501611
12.5%
I501232
12.5%
B501095
12.5%
C501074
12.5%
M501033
12.5%
E1634
 
< 0.1%
N616
 
< 0.1%
U547
 
< 0.1%
Other values (20)1946
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4014741
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O1002052
25.0%
A501901
12.5%
L501611
12.5%
I501232
12.5%
B501095
12.5%
C501074
12.5%
M501033
12.5%
E1634
 
< 0.1%
N616
 
< 0.1%
U547
 
< 0.1%
Other values (20)1946
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4014741
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O1002052
25.0%
A501901
12.5%
L501611
12.5%
I501232
12.5%
B501095
12.5%
C501074
12.5%
M501033
12.5%
E1634
 
< 0.1%
N616
 
< 0.1%
U547
 
< 0.1%
Other values (20)1946
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4014741
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O1002052
25.0%
A501901
12.5%
L501611
12.5%
I501232
12.5%
B501095
12.5%
C501074
12.5%
M501033
12.5%
E1634
 
< 0.1%
N616
 
< 0.1%
U547
 
< 0.1%
Other values (20)1946
 
< 0.1%
Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:42.482865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.913216099
Min length4

Characters and Unicode

Total characters3.468.561
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCASANARE
2nd rowCASANARE
3rd rowCASANARE
4th rowCASANARE
5th rowMETA
ValueCountFrequency (%)
valle116013
22.4%
santander63894
12.3%
tolima38913
 
7.5%
bolivar28298
 
5.5%
meta25539
 
4.9%
huila25246
 
4.9%
antioquia25066
 
4.8%
atlantico23733
 
4.6%
cundinamarca17421
 
3.4%
cordoba15532
 
3.0%
Other values (25)138006
26.7%
2025-10-28T22:37:42.736705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A721626
20.8%
L367518
10.6%
E270650
 
7.8%
N259826
 
7.5%
T233746
 
6.7%
I227167
 
6.5%
R216520
 
6.2%
O206547
 
6.0%
C155719
 
4.5%
V147493
 
4.3%
Other values (15)661749
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3468561
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A721626
20.8%
L367518
10.6%
E270650
 
7.8%
N259826
 
7.5%
T233746
 
6.7%
I227167
 
6.5%
R216520
 
6.2%
O206547
 
6.0%
C155719
 
4.5%
V147493
 
4.3%
Other values (15)661749
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3468561
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A721626
20.8%
L367518
10.6%
E270650
 
7.8%
N259826
 
7.5%
T233746
 
6.7%
I227167
 
6.5%
R216520
 
6.2%
O206547
 
6.0%
C155719
 
4.5%
V147493
 
4.3%
Other values (15)661749
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3468561
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A721626
20.8%
L367518
10.6%
E270650
 
7.8%
N259826
 
7.5%
T233746
 
6.7%
I227167
 
6.5%
R216520
 
6.2%
O206547
 
6.0%
C155719
 
4.5%
V147493
 
4.3%
Other values (15)661749
19.1%
Distinct1014
Distinct (%)0.2%
Missing2
Missing (%)< 0.1%
Memory size3.8 MiB
2025-10-28T22:37:43.025787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length33
Mean length8.358278506
Min length4

Characters and Unicode

Total characters4.193.574
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)< 0.1%

Sample

1st rowTAURAMENA
2nd rowAGUAZUL
3rd rowTAURAMENA
4th rowYOPAL
5th rowACACIAS
ValueCountFrequency (%)
cali52362
 
8.4%
san20100
 
3.2%
ibague17461
 
2.8%
bucaramanga15632
 
2.5%
de15623
 
2.5%
barranquilla14491
 
2.3%
cartagena12059
 
1.9%
villavicencio11432
 
1.8%
la11226
 
1.8%
el10431
 
1.7%
Other values (1037)440990
70.9%
2025-10-28T22:37:43.454803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A804312
19.2%
I357871
 
8.5%
L312875
 
7.5%
E290180
 
6.9%
R286923
 
6.8%
O252434
 
6.0%
C248142
 
5.9%
N247172
 
5.9%
U200773
 
4.8%
T146446
 
3.5%
Other values (26)1046446
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)4193574
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A804312
19.2%
I357871
 
8.5%
L312875
 
7.5%
E290180
 
6.9%
R286923
 
6.8%
O252434
 
6.0%
C248142
 
5.9%
N247172
 
5.9%
U200773
 
4.8%
T146446
 
3.5%
Other values (26)1046446
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4193574
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A804312
19.2%
I357871
 
8.5%
L312875
 
7.5%
E290180
 
6.9%
R286923
 
6.8%
O252434
 
6.0%
C248142
 
5.9%
N247172
 
5.9%
U200773
 
4.8%
T146446
 
3.5%
Other values (26)1046446
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4193574
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A804312
19.2%
I357871
 
8.5%
L312875
 
7.5%
E290180
 
6.9%
R286923
 
6.8%
O252434
 
6.0%
C248142
 
5.9%
N247172
 
5.9%
U200773
 
4.8%
T146446
 
3.5%
Other values (26)1046446
25.0%
Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:43.622419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.911563812
Min length4

Characters and Unicode

Total characters3.467.732
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCASANARE
2nd rowCASANARE
3rd rowCASANARE
4th rowCASANARE
5th rowMETA
ValueCountFrequency (%)
valle117121
22.6%
santander64412
12.4%
tolima37612
 
7.3%
bolivar27322
 
5.3%
meta25525
 
4.9%
huila25438
 
4.9%
antioquia24580
 
4.7%
atlantico24039
 
4.6%
cundinamarca17340
 
3.3%
cordoba16302
 
3.1%
Other values (24)138351
26.7%
2025-10-28T22:37:43.895254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A719919
20.8%
L367600
10.6%
E272162
 
7.8%
N260777
 
7.5%
T234138
 
6.8%
I223571
 
6.4%
R216748
 
6.3%
O207797
 
6.0%
C154437
 
4.5%
V147616
 
4.3%
Other values (14)662967
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3467732
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A719919
20.8%
L367600
10.6%
E272162
 
7.8%
N260777
 
7.5%
T234138
 
6.8%
I223571
 
6.4%
R216748
 
6.3%
O207797
 
6.0%
C154437
 
4.5%
V147616
 
4.3%
Other values (14)662967
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3467732
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A719919
20.8%
L367600
10.6%
E272162
 
7.8%
N260777
 
7.5%
T234138
 
6.8%
I223571
 
6.4%
R216748
 
6.3%
O207797
 
6.0%
C154437
 
4.5%
V147616
 
4.3%
Other values (14)662967
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3467732
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A719919
20.8%
L367600
10.6%
E272162
 
7.8%
N260777
 
7.5%
T234138
 
6.8%
I223571
 
6.4%
R216748
 
6.3%
O207797
 
6.0%
C154437
 
4.5%
V147616
 
4.3%
Other values (14)662967
19.1%
Distinct870
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2025-10-28T22:37:44.156407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length26
Mean length8.200897297
Min length4

Characters and Unicode

Total characters4.114.628
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)< 0.1%

Sample

1st rowTAURAMENA
2nd rowAGUAZUL
3rd rowYOPAL
4th rowYOPAL
5th rowACACIAS
ValueCountFrequency (%)
cali58203
 
9.7%
bucaramanga23124
 
3.8%
ibague18397
 
3.1%
barranquilla17388
 
2.9%
san16041
 
2.7%
cartagena14340
 
2.4%
de12867
 
2.1%
villavicencio12552
 
2.1%
neiva11237
 
1.9%
la10532
 
1.8%
Other values (886)406732
67.6%
2025-10-28T22:37:44.548547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A815308
19.8%
I356606
 
8.7%
L313119
 
7.6%
E279790
 
6.8%
R279747
 
6.8%
C258830
 
6.3%
N245074
 
6.0%
O231284
 
5.6%
U202700
 
4.9%
T144028
 
3.5%
Other values (21)988142
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)4114628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A815308
19.8%
I356606
 
8.7%
L313119
 
7.6%
E279790
 
6.8%
R279747
 
6.8%
C258830
 
6.3%
N245074
 
6.0%
O231284
 
5.6%
U202700
 
4.9%
T144028
 
3.5%
Other values (21)988142
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4114628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A815308
19.8%
I356606
 
8.7%
L313119
 
7.6%
E279790
 
6.8%
R279747
 
6.8%
C258830
 
6.3%
N245074
 
6.0%
O231284
 
5.6%
U202700
 
4.9%
T144028
 
3.5%
Other values (21)988142
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4114628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A815308
19.8%
I356606
 
8.7%
L313119
 
7.6%
E279790
 
6.8%
R279747
 
6.8%
C258830
 
6.3%
N245074
 
6.0%
O231284
 
5.6%
U202700
 
4.9%
T144028
 
3.5%
Other values (21)988142
24.0%